US10116516B2 - Network topology discovery method and device - Google Patents

Network topology discovery method and device Download PDF

Info

Publication number
US10116516B2
US10116516B2 US15/429,946 US201715429946A US10116516B2 US 10116516 B2 US10116516 B2 US 10116516B2 US 201715429946 A US201715429946 A US 201715429946A US 10116516 B2 US10116516 B2 US 10116516B2
Authority
US
United States
Prior art keywords
link
links
same
port
network
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active, expires
Application number
US15/429,946
Other languages
English (en)
Other versions
US20170155556A1 (en
Inventor
Yulin Yuan
Xiaoji Fan
Zhiming Ye
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Huawei Technologies Co Ltd
Original Assignee
Huawei Technologies Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Huawei Technologies Co Ltd filed Critical Huawei Technologies Co Ltd
Publication of US20170155556A1 publication Critical patent/US20170155556A1/en
Assigned to HUAWEI TECHNOLOGIES CO., LTD. reassignment HUAWEI TECHNOLOGIES CO., LTD. ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: YUAN, Yulin, FAN, XIAOJI, YE, ZHIMING
Application granted granted Critical
Publication of US10116516B2 publication Critical patent/US10116516B2/en
Active legal-status Critical Current
Adjusted expiration legal-status Critical

Links

Images

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/12—Discovery or management of network topologies
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/14—Network analysis or design
    • H04L41/142—Network analysis or design using statistical or mathematical methods
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L43/00—Arrangements for monitoring or testing data switching networks
    • H04L43/08—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L43/00—Arrangements for monitoring or testing data switching networks
    • H04L43/16—Threshold monitoring
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L45/00—Routing or path finding of packets in data switching networks
    • H04L45/02—Topology update or discovery
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/30—

Definitions

  • Embodiments of the present application relate to the field of network connection detection, and in particular, to a network topology discovery method and device.
  • an operator needs to perform evaluation and optimization on a network and analyze a network element in the network and a service status. For example, the operator needs to collect configuration information of a network element, collect traffic information of a port, evaluate a capacity of the port, discover a port with overloaded traffic, and perform capacity expansion or adjust a flow path for the port.
  • an optimization and analysis tool is used to perform the network evaluation and optimization, generating a network topology needs to depend on the optimization and analysis tool. Network traffic evaluation, service evaluation, and emulation can be performed only based on the network topology and a result of network evaluation and analysis is displayed based on the network topology.
  • a network topology discovery method in the prior art is collecting network characteristic data of a network element in a to-be-analyzed network, obtaining, by means of calculation according to the collected network characteristic data and a corresponding network topology discovery algorithm, a link set corresponding to the algorithm, and further obtaining a network topology.
  • a network topology based on a port Internet Protocol (IP) address characteristic is obtained by means of calculation according to a port IP address and an IP address matching algorithm
  • a network topology based on a port alias characteristic is obtained by means of calculation according to a port alias and a port alias matching algorithm
  • a network topology of a network established based on Cisco devices may be obtained according to the Cisco Discovery Protocol (CDP), or the like.
  • Embodiments of the present application provide a network topology discovery method and device, which can perform a comprehensive analysis on results obtained after network topology discovery is performed by using multiple types of network characteristic data, and improve accuracy of network topology discovery.
  • an embodiment of the present application provides a network topology discovery method, where the method includes:
  • obtaining a second link set by performing an operation on the first link set, where the operation includes: combining same links, and for at least two links having only one same port, retaining a link having a largest confidence value in the at least two links and deleting a remaining link, where the same links are at least two links in which two ports included in one link are the same as those in any other link; and
  • the combining same links includes:
  • the operation further includes: after the retaining a link having a largest confidence value in the at least two links and deleting a remaining link,
  • the uncertainty reasoning algorithm includes the following:
  • CF i (H) is a confidence value among multiple confidence values of the same links
  • CF j (H) is another confidence value among the multiple confidence values of the same links
  • CF i,j (H) is a new confidence value of the same links that is calculated according to CF i (H) and CF j (H).
  • the network characteristic data and the corresponding topology discovery algorithm include at least two types of the following combinations: a port Internet Protocol (IP) address and an IP address matching algorithm, a port alias and a port alias matching algorithm, or port Link Layer Discovery Protocol (LLDP) neighbor information and a port LLDP link algorithm.
  • IP Internet Protocol
  • LLDP Link Layer Discovery Protocol
  • an embodiment of the present application provides a network topology discovery device, where the device includes:
  • a collection unit configured to collect network characteristic data of all network elements in a to-be-analyzed network
  • a link obtaining unit configured to obtain at least two corresponding link subsets respectively by using at least two types of topology discovery algorithms and according to the network characteristic data, and gather all links in the at least two link subsets into one set to obtain a first link set, where a confidence value of a link in each link subset is equal to a confidence value of a topology discovery algorithm corresponding to the link subset, confidence values of different topology discovery algorithms are different, and the link is a link that consists of two ports of different network elements;
  • a link processing unit configured to obtain a second link set by performing an operation on the first link set, where the operation includes: combining same links, and for at least two links having only one same port, retaining a link having a largest confidence value in the at least two links and deleting a remaining link, where the same links are at least two links in which two ports included in one link are the same as those in any other link; and
  • a topology obtaining unit configured to obtain a network topology of the to-be-analyzed network according to each link in the second link set.
  • the link processing unit is specifically configured to:
  • the second link set by performing an operation on the first link set, where the operation includes: combining same links in the first link set, calculating, according to multiple confidence values of the same links and an uncertainty reasoning algorithm, a confidence value of the retained link after combining, and for the at least two links having only one same port, retaining the link having the largest confidence value in the at least two links and deleting the remaining link, where the same links are at least two links in which two ports included in one link are the same as those in any other link.
  • the link processing unit is specifically configured to:
  • the operation includes: combining same links in the first link set, calculating, according to multiple confidence values of the same links and an uncertainty reasoning algorithm, a confidence value of the retained link after combining, and for the at least two links having only one same port, retaining the link having the largest confidence value in the at least two links, deleting the remaining link, comparing the confidence values of the links in the first link set with a preset threshold, and selecting a link whose confidence value is greater than the preset threshold, where the same links are at least two links in which two ports included in one link are the same as those in any other link.
  • the uncertainty reasoning algorithm includes the following:
  • CF i (H) is a confidence value among multiple confidence values of the same links
  • CF j (H) is another confidence value among the multiple confidence values of the same links
  • CF i,j (H) is a new confidence value of the same links that is calculated according to CF i (H) and CF j (H).
  • the network characteristic data and the corresponding topology discovery algorithm include at least two types of the following combinations: a port Internet Protocol (IP) address and an IP address matching algorithm, a port alias and a port alias matching algorithm, or port Link Layer Discovery Protocol (LLDP) neighbor information and a port LLDP link algorithm.
  • IP Internet Protocol
  • LLDP Link Layer Discovery Protocol
  • network characteristic data of all network elements in a to-be-analyzed network is collected; next, at least two corresponding link subsets are obtained respectively by using at least two types of topology discovery algorithms and according to the network characteristic data, and all links in the at least two link subsets are gathered into one set to obtain a first link set; then, a second link set is obtained by performing an operation on the first link set, where the operation includes: combining same links, and for at least two links having only one same port, retaining a link having a largest confidence value in the at least two links and deleting a remaining link, where the same links are at least two links in which two ports included in one link are the same as those in any other link; and last, a network topology of the to-be-analyzed network is obtained according to each link in the second link set.
  • a comprehensive analysis may be performed on results obtained after network topology discovery is performed by using multiple types of network characteristic data, so as to improve
  • FIG. 1 is a schematic flowchart 1 of a network topology discovery method according to an embodiment of the present application
  • FIG. 2 is a schematic diagram of effects of a actual network topology and physical network topologies obtained by using three types of topology discovery algorithms according to an embodiment of the present application;
  • FIG. 3 is a schematic flowchart 2 of a network topology discovery method according to an embodiment of the present application.
  • FIG. 4 is a schematic structural diagram 1 of a network topology discovery device according to an embodiment of the present application.
  • FIG. 5 is a schematic structural diagram 2 of a network topology discovery device according to an embodiment of the present application.
  • An embodiment of the present application provides a network topology discovery method. As shown in FIG. 1 , the method includes:
  • Step 101 Collect network characteristic data of all network elements in a to-be-analyzed network.
  • Step 102 Obtain at least two corresponding link subsets respectively by using at least two types of topology discovery algorithms and according to the network characteristic data, and gather all links in the at least two link subsets into one set to obtain a first link set.
  • a confidence value of a link in each link subset is equal to a confidence value of a topology discovery algorithm corresponding to each link subset, confidence values of different topology discovery algorithms are different, and the link is a link that consists of two ports of different network elements.
  • Step 103 Obtain a second link set by performing an operation on the first link set, where the operation includes: combining same links, and for at least two links having only one same port, retaining a link having a largest confidence value in the at least two links and deleting a remaining link, where the same links are at least two links in which two ports included in one link are the same as those in any other link.
  • Step 104 Obtain a network topology of the to-be-analyzed network according to each link in the second link set.
  • network characteristic data of all network elements in a to-be-analyzed network is collected; next, at least two corresponding link subsets are obtained respectively by using at least two types of topology discovery algorithms and according to the network characteristic data, and all links in the at least two link subsets are gathered into one set to obtain a first link set; and a second link set is obtained by performing an operation on the first link set, where the operation includes: combining same links, and for at least two links having only one same port, retaining a link having a largest confidence value in the at least two links and deleting a remaining link, where the same links are at least two links in which two ports included in one link are the same as those in any other link; and last, a network topology of the to-be-analyzed network is obtained according to each link in the second link set.
  • a comprehensive analysis may be performed on results obtained after network topology discovery is performed by using multiple types of network characteristic data, so as to improve accuracy of network top
  • a network element is a network unit or node in a network system, and the unit is a device that can independently complete one or more functions.
  • a base station is a network element; an entity that can independently complete a function may become a network element, and therefore, a switch, a router or the like is also a network element; a link may be a physical link or a logical link.
  • Network characteristic data of all network elements in the to-be-analyzed network may be collected by using a collection tool, and the network characteristic data is used as data input of a topology discovery algorithm. It is definite that the foregoing collecting network characteristic data of network elements by using a collection tool can be implemented by all persons of ordinary skills in the art.
  • the network characteristic data includes at least two of the following: a port IP address, a port alias, port LLDP neighbor information, a network element name, port traffic, a Media Access Control (MAC) forwarding table, an Address Resolution Protocol (ARP) forwarding table, a routing and forwarding table, or virtual local area network (VLAN) configuration information.
  • network characteristic data to be selected for collection is a port IP address, a port alias, and port LLDP neighbor information; and correspondingly, topology discovery algorithms to be used are an IP address matching algorithm, a port alias algorithm, and a port LLDP link algorithm. It should be noted that such a selection manner is only exemplary, and is intended only to help describe the technical solution in this embodiment, and in actual application, persons skilled in the art may collect, according to an actual requirement, network characteristic data and select a corresponding topology algorithm.
  • FIG. 3 shows a network topology discovery method that is provided in this embodiment of the present application and that is based on the foregoing content.
  • the method includes:
  • Step 201 Collect network characteristic data of all network elements in a to-be-analyzed network.
  • a port IP address, a port alias, and port LLDP neighbor information that are of each of 20 ports of 5 network elements in the to-be-analyzed network are collected.
  • Step 202 Obtain at least two corresponding link subsets respectively by using at least two types of topology discovery algorithms and according to the network characteristic data, and gather all links in the at least two link subsets into one set to obtain a first link set.
  • a confidence value of a link in each link subset is equal to a confidence value of a topology discovery algorithm corresponding to each link subset, confidence values of different topology discovery algorithms are different, and the link is a link that consists of two ports of different network elements.
  • the port IP addresses of the 20 ports are used as input of the IP address matching algorithm, a link subset L1 corresponding to the algorithm is obtained by means of calculation, and sequentially, a corresponding link subset L2 is obtained by means of calculation according to the port aliases and the port alias matching algorithm and a corresponding link subset L3 is obtained by means of calculation according to the port LLDP neighbor information and the port LLDP link algorithm.
  • Case 1 Two or more same links exist, where two same links mean that two ports included in one link are the same as those in the other link.
  • Case 2 Two or more links having only one same port exist.
  • Different confidence values CFs are set according to different topology discovery algorithms (or corresponding network characteristic data), and a range of the confidence value CF may be set to [ ⁇ 1, 1].
  • confidence values of all links in a link set obtained by means of calculation according to one topology discovery algorithm are the same as a confidence value of the topology discovery algorithm (or corresponding network characteristic data).
  • Step 203 Combine same links in the first link set, and calculate, according to respective confidence values of the multiple same links and an uncertainty reasoning algorithm, a confidence value of the retained link after combining.
  • the multiple same links are combined, that is, only one same link in the first link set G is retained, and the confidence value of the retained link after combining is calculated according to respective confidence values of the multiple same links and the uncertainty reasoning algorithm.
  • the same links are at least two links in which two ports included in one link are the same as those in any other link, for example, L12, L22 and L31 in the foregoing Table 4.
  • links L12, L22 and L31 are combined, the following describes how a confidence value of the retained L12 after combining is calculated according to the uncertainty reasoning algorithm.
  • the uncertainty reasoning algorithm in this embodiment of the present application is based on a confidence value, and the uncertainty reasoning algorithm includes the following:
  • CF i (H) is a confidence value among multiple confidence values of the same links
  • CF j (H) is another confidence value among the multiple confidence values of the same links
  • CF i,j (H) is a new confidence value of the same links that is calculated according to CF i (H) and CF j (H).
  • CF i , j ⁇ ( H ) CF i ⁇ ( H ) + CF j ⁇ ( H ) 1 - min ⁇ ( ⁇ CF i ⁇ ( H ) ⁇ , ⁇ CF j ⁇ ( H ) ⁇ ) .
  • the confidence value of the retained link L12 after combining is calculated.
  • a method for calculating a confidence value of a retained link after combining is not limited to the foregoing calculation formulas, and persons skilled in the art may further perform calculation by using another calculation method.
  • a weighted sum algorithm may be used.
  • a method for calculating a comprehensive confidence value of a link according to multiple confidence values of the same links is not limited in this embodiment of the present application, and persons of ordinary skill in the art may use the calculation method provided in this embodiment of the present application, or may use another calculation method.
  • Step 204 For at least two links having only one same port in the first link set, retain a link having a largest confidence value in the at least two links and delete a remaining link.
  • Step 205 Compare the confidence values of the links in the first link set with a preset threshold, and select a link whose confidence value is greater than the preset threshold, so as to obtain a second link set.
  • the confidence values, of all links in the first link set, obtained according to step 203 and step 204 are compared with a preset threshold 0.7, and a link whose confidence value is greater than 0.7 is selected, so as to obtain a second link set G′.
  • a preset threshold 0.7 a link whose confidence value is greater than 0.7 is selected, so as to obtain a second link set G′.
  • Step 206 Obtain a network topology of the to-be-analyzed network according to each link in the second link set.
  • the network topology of the to-be-analyzed network can be obtained according to all links L11(P11, P21), L12(P22, P32), L13(P33, P43), and L33(P44, P54) in the second link set G′: the port P11 of the network element N1 is connected to the port 21 of the network element N2, the port P22 of the network element N2 is connected to the port P32 of the network element N3, the port P33 of the network element N3 is connected to the port P43 of the network element N4, and the port P44 of the network element N4 is connected to the port P54 of the network element N5. It can be seen that the network topology of the to-be-analyzed network that is obtained according to the technical solution provided in the foregoing embodiment is consistent with the network topology of the to-be-analyzed network shown in FIG. 2 .
  • network characteristic data of all network elements in a to-be-analyzed network is collected; next, at least two corresponding link subsets are obtained respectively by using at least two types of topology discovery algorithms and according to the network characteristic data, and all links in the at least two link subsets are gathered into one set to obtain a first link set; then, a second link set is obtained by performing an operation on the first link set, where the operation includes: combining same links, and for at least two links having only one same port, retaining a link having a largest confidence value in the at least two links and deleting a remaining link, where the same links are at least two links in which two ports included in one link are the same as those in any other link; and last, a network topology of the to-be-analyzed network is obtained according to each link in the second link set.
  • a comprehensive analysis may be performed on results obtained after network topology discovery is performed by using multiple types of network characteristic data, so as to improve accuracy of network
  • An embodiment of the present application provides a network topology discovery device 00 .
  • the device 00 includes:
  • a collection unit 10 configured to collect network characteristic data of all network elements in a to-be-analyzed network
  • a link obtaining unit 20 configured to obtain at least two corresponding link subsets respectively by using at least two types of topology discovery algorithms and according to the network characteristic data, and gather all links in the at least two link subsets into one set to obtain a first link set, where a confidence value of a link in each link subset is equal to a confidence value of a topology discovery algorithm corresponding to each link subset, confidence values of different topology discovery algorithms are different, and the link is a link that consists of two ports of different network elements;
  • a link processing unit 30 configured to obtain a second link set by performing an operation on the first link set, where the operation includes: combining same links, and for at least two links having only one same port, retaining a link having a largest confidence value in the at least two links and deleting a remaining link, where the same links are at least two links in which two ports included in one link are the same as those in any other link; and
  • a topology obtaining unit 40 configured to obtain a network topology of the to-be-analyzed network according to each link in the second link set.
  • the link processing unit 30 is specifically configured to:
  • the second link set by performing an operation on the first link set, where the operation includes: combining same links in the first link set, calculating, according to multiple confidence values of the same links and an uncertainty reasoning algorithm, a confidence value of the retained link after combining, and for the at least two links having only one same port, retaining the link having the largest confidence value in the at least two links and deleting the remaining link, where the same links are at least two links in which two ports included in one link are the same as those in any other link.
  • the link processing unit 30 may be further specifically configured to:
  • the operation includes: combining same links in the first link set, calculating, according to multiple confidence values of the same links and an uncertainty reasoning algorithm, a confidence value of the retained link after combining, and for the at least two links having only one same port, retaining the link having the largest confidence value in the at least two links, deleting the remaining link, comparing the confidence values of the links in the first link set with a preset threshold, and selecting a link whose confidence value is greater than the preset threshold, where the same links are at least two links in which two ports included in one link are the same as those in any other link.
  • the uncertainty reasoning algorithm includes the following:
  • CF i (H) is a confidence value among multiple confidence values of the same links
  • CF j (H) is another confidence value among the multiple confidence values of the same links
  • CF i,j (H) is a new confidence value of the same links that is calculated according to CF i (H) and CF j (H).
  • the network characteristic data and the corresponding topology discovery algorithm include at least two types of the following combinations: a port Internet Protocol IP address and an Internet Protocol IP address matching algorithm, a port alias and a port alias matching algorithm, or port Link Layer Discovery Protocol LLDP neighbor information and a port Link Layer Discovery Protocol LLDP link algorithm.
  • network characteristic data of all network elements in a to-be-analyzed network is collected; next, at least two corresponding link subsets are obtained respectively by using at least two types of topology discovery algorithms and according to the network characteristic data, and all links in the at least two link subsets are gathered into one set to obtain a first link set; then, a second link set is obtained by performing an operation on the first link set, where the operation includes: combining same links, and for at least two links having only one same port, retaining a link having a largest confidence value in the at least two links and deleting a remaining link, where the same links are at least two links in which two ports included in one link are the same as those in any other link; and last, a network topology of the to-be-analyzed network is obtained according to each link in the second link set.
  • a comprehensive analysis may be performed on results obtained after network topology discovery is performed by using multiple types of network characteristic data, so as to improve accuracy of network
  • An embodiment of the present application further provides a network topology discovery device 90 .
  • the device 90 includes: a bus 94 and a processor 91 , a memory 92 , and an interface 93 that are connected to the bus 94 , where the interface 93 is configured to communicate, the memory 92 is configured to store an instruction, and the processor 91 is configured to execute the instruction to:
  • a second link set by performing an operation on the first link set, where the operation includes: combining same links, and for at least two links having only one same port, retaining a link having a largest confidence value in the at least two links and deleting a remaining link, where the same links are at least two links in which two ports included in one link are the same as those in any other link; and
  • processor 91 executes the instruction to combine same links may specifically include:
  • the processor 91 executes the instruction to obtain the second link set by performing the operation on the first link set, and the operation further includes: after retaining a link having a largest confidence value in the at least two links and deleting a remaining link,
  • the uncertainty reasoning algorithm includes the following:
  • CF i (H) is a confidence value among multiple confidence values of the same links
  • CF j (H) is another confidence value among the multiple confidence values of the same links
  • CF i,j (H) is a new confidence value of the same links that is calculated according to CF i (H) and CF j (H).
  • the network characteristic data and the corresponding topology discovery algorithm include at least two types of the following combinations: a port Internet Protocol (IP) address and an IP address matching algorithm, a port alias and a port alias matching algorithm, or port Link Layer Discovery Protocol (LLDP) neighbor information and a port LLDP link algorithm.
  • IP Internet Protocol
  • LLDP Link Layer Discovery Protocol
  • network characteristic data of all network elements in a to-be-analyzed network is collected; next, at least two corresponding link subsets are obtained respectively by using at least two types of topology discovery algorithms and according to the network characteristic data, and all links in the at least two link subsets are gathered into one set to obtain a first link set; then, a second link set is obtained by performing an operation on the first link set, where the operation includes: combining same links, and for at least two links having only one same port, retaining a link having a largest confidence value in the at least two links and deleting a remaining link, where the same links are at least two links in which two ports included in one link are the same as those in any other link; and last, a network topology of the to-be-analyzed network is obtained according to each link in the second link set.
  • a comprehensive analysis may be performed on results obtained after network topology discovery is performed by using multiple types of network characteristic data, so as to improve accuracy of network
  • the program may be stored in a computer-readable storage medium.
  • the foregoing storage medium includes: any medium that can store program code, such as a ROM, a RAM, a magnetic disk, or an optical disc.

Landscapes

  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Physics & Mathematics (AREA)
  • Algebra (AREA)
  • General Physics & Mathematics (AREA)
  • Mathematical Analysis (AREA)
  • Mathematical Optimization (AREA)
  • Mathematical Physics (AREA)
  • Probability & Statistics with Applications (AREA)
  • Pure & Applied Mathematics (AREA)
  • Environmental & Geological Engineering (AREA)
  • Data Exchanges In Wide-Area Networks (AREA)
US15/429,946 2014-08-12 2017-02-10 Network topology discovery method and device Active 2035-10-09 US10116516B2 (en)

Applications Claiming Priority (4)

Application Number Priority Date Filing Date Title
CN201410395692.4A CN104125154B (zh) 2014-08-12 2014-08-12 网络拓扑发现方法和设备
CN201410395692 2014-08-12
CN201410395692.4 2014-08-12
PCT/CN2015/086155 WO2016023438A1 (zh) 2014-08-12 2015-08-05 网络拓扑发现方法和设备

Related Parent Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2015/086155 Continuation WO2016023438A1 (zh) 2014-08-12 2015-08-05 网络拓扑发现方法和设备

Publications (2)

Publication Number Publication Date
US20170155556A1 US20170155556A1 (en) 2017-06-01
US10116516B2 true US10116516B2 (en) 2018-10-30

Family

ID=51770422

Family Applications (1)

Application Number Title Priority Date Filing Date
US15/429,946 Active 2035-10-09 US10116516B2 (en) 2014-08-12 2017-02-10 Network topology discovery method and device

Country Status (5)

Country Link
US (1) US10116516B2 (zh)
EP (1) EP3182656B1 (zh)
CN (1) CN104125154B (zh)
ES (1) ES2745541T3 (zh)
WO (1) WO2016023438A1 (zh)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11262990B2 (en) 2020-05-26 2022-03-01 International Business Machines Corporation Application topology discovery

Families Citing this family (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104125154B (zh) 2014-08-12 2017-09-26 华为技术有限公司 网络拓扑发现方法和设备
JP6700674B2 (ja) * 2015-05-21 2020-05-27 三菱重工サーマルシステムズ株式会社 電動圧縮機用モータハウジングおよびそれを用いた車載用電動圧縮機
CN105072039A (zh) * 2015-07-31 2015-11-18 山东蚁巡网络科技有限公司 一种链路层网络拓扑发现方法
EP3552353B1 (en) 2016-12-09 2024-04-10 Robert Bosch GmbH Computer network and method for running a computer network
US10447553B2 (en) * 2017-05-05 2019-10-15 Servicenow, Inc. Systems and methods for service-aware mapping of a system infrastructure
US10425281B2 (en) * 2017-11-10 2019-09-24 Cisco Technology, Inc. Automated network entity replacement based on historical topology consciousness
CN109150640B (zh) * 2018-11-08 2021-07-09 中电莱斯信息系统有限公司 一种基于二层网络协议的网络拓扑发现方法及系统
CN109842520B (zh) 2018-12-27 2021-06-22 华为技术有限公司 网络拓扑的确定方法、装置及系统
CN110149225B (zh) * 2019-05-13 2022-01-21 中国联合网络通信集团有限公司 网络拓扑结构评估方法及装置
CN112671567B (zh) * 2020-12-16 2023-02-21 中盈优创资讯科技有限公司 一种基于服务化接口的5g核心网拓扑发现方法及装置
CN113364628A (zh) * 2021-06-11 2021-09-07 上海中通吉网络技术有限公司 服务器与交换机拓扑关系建立方法及设备

Citations (23)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20020165927A1 (en) 2001-04-20 2002-11-07 Discreet Logic Inc. Image processing
US20020165957A1 (en) 2001-05-02 2002-11-07 Devoe Jiva Gandhara Intelligent dynamic route selection based on active probing of network operational characteristics
US6646989B1 (en) * 1998-06-29 2003-11-11 Lucent Technologies Inc. Hop-by-hop routing with node-dependent topology information
US20040255184A1 (en) * 2003-05-27 2004-12-16 Lucent Technologies Inc. System and method for determining the physical topology of a network having multiple subnets
US20060056328A1 (en) * 2002-07-30 2006-03-16 Lehane Andrew R Identifying network rotuters and paths
US20060256733A1 (en) * 2005-05-12 2006-11-16 Yigal Bejerano Methods and devices for discovering the topology of large multi-subnet LANs
CN1921419A (zh) 2005-08-25 2007-02-28 英业达股份有限公司 网络物理结构的拓扑方法
US20080170513A1 (en) * 2007-01-12 2008-07-17 Samsung Electronics Co., Ltd Group communication in a mobile AD-HOC network
US20090116404A1 (en) 2007-11-01 2009-05-07 Telefonaktiebolaget Lm Ericsson (Publ) Topology discovery in heterogeneous networks
WO2009118050A1 (en) 2008-03-28 2009-10-01 Telefonaktiebolaget Lm Ericsson (Publ) End-to-end inter-domain routing
CN101640612A (zh) 2009-09-07 2010-02-03 杭州华三通信技术有限公司 一种流量路径发现与故障快速定位的方法及装置
US20100208662A1 (en) * 2009-02-13 2010-08-19 Miraveo, Inc. Systems and Methods for Creating, Managing and Communicating Users and Applications on Spontaneous Area Networks
CN101874388A (zh) 2007-10-31 2010-10-27 爱立信电话股份有限公司 在节点之间具有多个路径的网络和用于这样的网络的节点
CN101917310A (zh) 2010-08-27 2010-12-15 电子科技大学 基于流量分析的网络拓扑推测方法
US20110317710A1 (en) * 2009-12-21 2011-12-29 Thales Enhanced reliability routing protocol
US20120113863A1 (en) * 2010-11-09 2012-05-10 Cisco Technology, Inc. Dynamic Wake-up Time Adjustment Based on Designated Paths Through a Computer Network
US20120131211A1 (en) 2010-11-24 2012-05-24 Verizon Patent And Licensing Inc. Optimized network device discovery
US8443065B1 (en) * 2010-11-08 2013-05-14 Adtran, Inc. System and method for locating, identifying and provisioning newly deployed network devices
CN103828310A (zh) 2013-11-29 2014-05-28 华为技术有限公司 一种链路发现的方法、系统及设备
CN104125154A (zh) 2014-08-12 2014-10-29 华为技术有限公司 网络拓扑发现方法和设备
US20170149619A1 (en) * 2014-08-12 2017-05-25 Huawei Technologies Co., Ltd. Method and device for discovering network topology
US9705747B1 (en) * 2012-12-04 2017-07-11 Qualcomm Incorporated Distributed path selection in hybrid networks
US9749214B2 (en) * 2014-02-26 2017-08-29 Futurewei Technologies, Inc. Software defined networking (SDN) specific topology information discovery

Patent Citations (26)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6646989B1 (en) * 1998-06-29 2003-11-11 Lucent Technologies Inc. Hop-by-hop routing with node-dependent topology information
US20020165927A1 (en) 2001-04-20 2002-11-07 Discreet Logic Inc. Image processing
US20020165957A1 (en) 2001-05-02 2002-11-07 Devoe Jiva Gandhara Intelligent dynamic route selection based on active probing of network operational characteristics
US20060056328A1 (en) * 2002-07-30 2006-03-16 Lehane Andrew R Identifying network rotuters and paths
US20040255184A1 (en) * 2003-05-27 2004-12-16 Lucent Technologies Inc. System and method for determining the physical topology of a network having multiple subnets
US20060256733A1 (en) * 2005-05-12 2006-11-16 Yigal Bejerano Methods and devices for discovering the topology of large multi-subnet LANs
CN1921419A (zh) 2005-08-25 2007-02-28 英业达股份有限公司 网络物理结构的拓扑方法
US20080170513A1 (en) * 2007-01-12 2008-07-17 Samsung Electronics Co., Ltd Group communication in a mobile AD-HOC network
CN101874388A (zh) 2007-10-31 2010-10-27 爱立信电话股份有限公司 在节点之间具有多个路径的网络和用于这样的网络的节点
US20160112324A1 (en) 2007-10-31 2016-04-21 Telefonaktiebolaget L M Ericsson (Publ) Networks having multiple paths between nodes and nodes for such a network
US20090116404A1 (en) 2007-11-01 2009-05-07 Telefonaktiebolaget Lm Ericsson (Publ) Topology discovery in heterogeneous networks
WO2009118050A1 (en) 2008-03-28 2009-10-01 Telefonaktiebolaget Lm Ericsson (Publ) End-to-end inter-domain routing
US20100208662A1 (en) * 2009-02-13 2010-08-19 Miraveo, Inc. Systems and Methods for Creating, Managing and Communicating Users and Applications on Spontaneous Area Networks
CN101640612A (zh) 2009-09-07 2010-02-03 杭州华三通信技术有限公司 一种流量路径发现与故障快速定位的方法及装置
US20110317710A1 (en) * 2009-12-21 2011-12-29 Thales Enhanced reliability routing protocol
CN101917310A (zh) 2010-08-27 2010-12-15 电子科技大学 基于流量分析的网络拓扑推测方法
US8443065B1 (en) * 2010-11-08 2013-05-14 Adtran, Inc. System and method for locating, identifying and provisioning newly deployed network devices
US20120113863A1 (en) * 2010-11-09 2012-05-10 Cisco Technology, Inc. Dynamic Wake-up Time Adjustment Based on Designated Paths Through a Computer Network
US20120131211A1 (en) 2010-11-24 2012-05-24 Verizon Patent And Licensing Inc. Optimized network device discovery
US9705747B1 (en) * 2012-12-04 2017-07-11 Qualcomm Incorporated Distributed path selection in hybrid networks
CN103828310A (zh) 2013-11-29 2014-05-28 华为技术有限公司 一种链路发现的方法、系统及设备
US20160277279A1 (en) 2013-11-29 2016-09-22 Huawei Technologies Co., Ltd. Link discovery method, system, and device
US9749214B2 (en) * 2014-02-26 2017-08-29 Futurewei Technologies, Inc. Software defined networking (SDN) specific topology information discovery
CN104125154A (zh) 2014-08-12 2014-10-29 华为技术有限公司 网络拓扑发现方法和设备
US20170149619A1 (en) * 2014-08-12 2017-05-25 Huawei Technologies Co., Ltd. Method and device for discovering network topology
US20170155556A1 (en) 2014-08-12 2017-06-01 Huawei Technologies Co., Ltd. Network topology discovery method and device

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
Knertser et al., "Network Device Discovery," Degree project in Communication Systems, XP055481084, KTH Information and Communication Technology, Stockholm, Sweden, (Jun. 10, 2013).

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11262990B2 (en) 2020-05-26 2022-03-01 International Business Machines Corporation Application topology discovery

Also Published As

Publication number Publication date
ES2745541T3 (es) 2020-03-02
CN104125154B (zh) 2017-09-26
EP3182656B1 (en) 2019-06-26
WO2016023438A1 (zh) 2016-02-18
EP3182656A4 (en) 2017-08-23
EP3182656A1 (en) 2017-06-21
US20170155556A1 (en) 2017-06-01
CN104125154A (zh) 2014-10-29

Similar Documents

Publication Publication Date Title
US10116516B2 (en) Network topology discovery method and device
Xu et al. DDoS attack detection under SDN context
US8804490B2 (en) Controller placement for fast failover in the split architecture
US8560647B2 (en) Controller placement for split architecture networks
US9990243B2 (en) Using spanning tree protocol to determine a layer 2 topology of an ethernet type network
US10361923B2 (en) Method and device for discovering network topology
EP2552059B1 (en) Packet transfer system, control apparatus, transfer apparatus, method of creating processing rules, and program
CN103401786B (zh) 网络拓扑建立、路径控制、报文传输方法及装置、系统
US8279753B2 (en) Efficient determination of fast routes when voluminous data is to be sent from a single node to many destination nodes via other intermediate nodes
Sitanayah et al. A fault-tolerant relay placement algorithm for ensuring k vertex-disjoint shortest paths in wireless sensor networks
ES2689913T3 (es) Identificación de rutas tomadas a través de una red de dispositivos interconectados
CN101510841B (zh) 端到端流量识别方法和系统
CN106487558B (zh) 一种实现接入设备扩缩容的方法和装置
CN106982164A (zh) 一种网络拓扑发现方法及设备
CN111130870B (zh) 基于影响因子评估的战术移动自组网关键节点分析方法
CN105763444B (zh) 一种路由同步方法及装置
CN112787925B (zh) 拥塞信息收集方法、确定最优路径方法、网络交换机
Melo et al. MC-ANT: a multi-colony ant algorithm
EP4164187A1 (en) Load balancing method, apparatus and system
KR102001487B1 (ko) 소프트웨어 정의 네트워킹 제어 방법 및 이를 수행하는 컴퓨팅 장치
Baltra et al. Ingress point spreading: A new primitive for adaptive active network mapping
CN103780495B (zh) 构建传感器网络的方法及装置
Ma et al. An algorithm of physical network topology discovery in multi-VLANs
Nicol et al. Sdn resiliency to controller failure in mobile contexts
CN113746741B (zh) 一种存储设备网络的设置方法、装置、设备及可读介质

Legal Events

Date Code Title Description
AS Assignment

Owner name: HUAWEI TECHNOLOGIES CO., LTD., CHINA

Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:YUAN, YULIN;FAN, XIAOJI;YE, ZHIMING;SIGNING DATES FROM 20180715 TO 20180716;REEL/FRAME:046586/0662

STCF Information on status: patent grant

Free format text: PATENTED CASE

MAFP Maintenance fee payment

Free format text: PAYMENT OF MAINTENANCE FEE, 4TH YEAR, LARGE ENTITY (ORIGINAL EVENT CODE: M1551); ENTITY STATUS OF PATENT OWNER: LARGE ENTITY

Year of fee payment: 4